AI agents are starting to create subagents and redefine workflows

How a master agent generates new agents

An artificial intelligence system is programmed with a goal, access to tools, and action limits. When it receives a complex request, it can distribute parts of the work to other specialized programs, called subagents, each with a defined role.

For example, the coordinator agent can assign one subagent the task of searching for information, another to analyze data, and a third to review the final result. After execution, the master agent collects the answers and produces the consolidated output.

The creation of these assistants can follow different paths. In some environments, subagents come pre-built and configured by developers. In others, the AI itself generates instructions or parameters to create a new specialized agent.

The Claude Code documentation illustrates this capability: based on a description of the desired task, the system can generate a subagent that performs the specified activity.

What changes in the way work is organized

The main advantage is the division of processes that require different skills. This allows steps to be performed simultaneously, reducing the total time needed to complete independent tasks.

Humans no longer need to monitor each operation individually and can focus on defining goals, providing context, and evaluating the final result. Instead of executing each step, the user needs to know how to break down the problem and verify that the deliverables make sense.

Risks and costs of agent proliferation

Increasing the number of agents introduces new risks. If instructions are unclear, different subagents may produce incompatible answers, and an error can spread throughout the workflow.

Furthermore, each agent consumes computational resources and generates new calls to the AI model. Without execution limits, a cap on the number of simultaneous agents, and a budget, a simple task can become expensive and difficult to manage.

To address these issues, development platforms already offer mechanisms that limit the depth of delegation, the number of active agents at any given time, and the costs associated with execution.

The role of professionals in the era of subagents

As these capabilities advance, the ability to translate broad goals into clear instructions, choose which tasks can be delegated, and establish criteria for validating results becomes increasingly important.

Human oversight remains crucial, especially when agents access sensitive data, modify records, or perform actions with financial implications. Permissions should be proportional to the task, and sensitive operations may require approval before completion.